System and method for predicting the presence of rare earth elements

ABSTRACT

A system for predicting rare earth elements (REEs) in a feedstock sample includes a measurement instrument that records a measurement for a sample, a processor communicatively coupled to the measuring instrument, and a memory communicatively coupled to the processor and containing machine readable instructions that, when executed by the processor, cause the processor to correlate the measurement series using a model; and predict a presence of one or more rare earth element based at least in part on the correlation. A method for predicting rare earth elements includes measuring feedstock samples via XRF or PGNAA, to generate a measurements of elements of interest with a lower atomic weight than REEs; correlating the measurements with a model; and predicting a presence of one or more rare earth elements based at least in part on the correlation.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/650,773 filed Feb. 11, 2022, which claims priority to U.S.Provisional Application No. 63/148,292 filed Feb. 11, 2021, and U.S.Provisional Application No. 63/150,351 filed Feb. 17, 2021, both ofwhich are incorporated herein in their entirety by reference.

GOVERNMENTAL INTEREST

This application was developed in association with the U.S. Departmentof Energy under Contract No. DE-SC0021837. The U.S. Government hascertain rights in this application.

BACKGROUND

Rare earth elements (REE) are crucial materials in many electronicdevices, energy system components and military defense applications. Therare earth group of elements includes lanthanum (La), cerium (Ce),praseodymium (Pr), neodymium (Nd), samarium (Sm), europium (Eu),gadolinium (Gd), scandium (Sc), yttrium (Y), terbium (Tb), dysprosium(Dy), holmium (Ho), erbium (Er), thulium (Tm), ytterbium (Yb), andlutetium (Lu). REE include light rare earth elements (LREE) and heavyrare earth elements (HREE). LREE include lanthanum, cerium,praseodymium, neodymium, samarium, europium, and gadolinium. HREEinclude scandium, yttrium, terbium, dysprosium, holmium, erbium,thulium, ytterbium, and lutetium. Other elements experiencing increasingdemand in the production of high-tech devices include gallium andgermanium. Locating these elements in feedstock and mined materials canbe challenging.

Recently, coal and coal byproducts have been identified as a promisingsources of REE. However, methods for detecting these elements in afeedstock sample in the field are limited. It is often necessary tobring samples to laboratory-based equipment that is expensive andnon-portable. One lab-based method for detecting and measuring REE insamples is inductively-coupled plasma optical emission spectroscopy(ICP-OES), which require expensive instrumentation that is not fit fordeployable use near mining sites.

SUMMARY OF THE INVENTION

In a first aspect, a system and method predict REE levels in a feedstockwithout requiring direct measurement of the REEs. Sensors are combinedwith predictive algorithms to enable selective mining and sorting ofhigh rare earth element (REE) content coal and coal-related feedstocks.In embodiments, sensors may be incorporated in stationary and handhelddevices that may be used in a wide variety of locations, such as amining site or feedstock processing facility.

A method for predicting the presence of rare earth elements (REEs) in afeedstock includes measuring a feedstock sample using a spectrumanalyzer to generate measurements of elements of interest with a loweratomic weight than REEs; correlating the measurements with a model; andpredicting presence of one or more REEs based at least in part on thecorrelation.

A system for predicting the presence of rare earth elements in afeedstock includes a measuring instrument that records a measurement fora feedstock sample; a processor, communicatively coupled to themeasuring instrument; and a memory communicatively coupled to theprocessor and containing machine readable instructions that, whenexecuted by the processor, causes the processor to execute the method.

Sensors may include elemental analyzers such as X-ray fluorescence (XRF)analyzers and prompt gamma neutron activation analysis (PGNAA) sensors,which identify REE-rich layers in coal seams using a relatively simpleand inexpensive sensor technology. XRF is fast and field deployable andcan be employed in combination with selective mining and real-timeon-belt coal sorting using PGNAA to provide more efficientidentification of REE-rich feedstocks.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a table of constants for use with a model for predicting thepresence of rare earth elements (REEs), in embodiments.

FIGS. 2A-2B are tables of constants for use with another model forpredicting the presence of REEs, in embodiments.

FIG. 3 is a flowchart illustrating a method for predicting the presenceof REEs, in embodiments.

FIG. 4 shows a system for predicting the presence of REEs, inembodiments.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Spectral analysis can be used to identify the presence and quantity, orabundance, of elements in a feedstock, such as coal and coal byproducts.X-ray fluorescence (XRF) identifies elements by irradiating a samplewith high-energy X-rays and capturing the resulting fluorescence with aspectrum analyzer. Each element in the sample will exhibit acharacteristic fluorescence signature, which can be used to identify theelement and its abundance in the sample.

Another technique for spectral analysis is prompt-gamma neutronactivation analysis (PGNAA), which irradiates a sample with a beam ofneutrons. Elements in the sample will emit gamma rays with can bemeasured with a gamma ray spectrometer. While similar, XRF and PGNAA maycapture different information. For example, XRF measures limited depthsand surface areas whereas PGNAA is able to penetrate an entirecross-section of a sample. A spectrum captured by spectral analysis of asample is typically shown as wavelength vs. count rate, or number ofemissions per second, for example. A graph of the spectrum will showpeaks at various wavelengths, which can be associated elements in thesample.

Identifying the REE presence and abundance in a sample is best done asclose to the mining process as possible. Since REEs are rare, not allmined feedstocks contain any REEs or enough to justify extraction.Mixing of feedstocks during transport and/or processing can dilute theconcentration of REEs, which makes identification close to the point ofmining i.e., before a quantity of ore has been intermixed with otherore, beneficial. REEs are among the heaviest atoms in the periodictable. Thus, detecting REEs with spectral analysis requires irradiatinga sample with higher energy particles to generate emissions that can becaptured with a spectrometer. Providing these higher energy particlescan be difficult or impossible in the field or at locations, whereidentifying REE content of a sample provides the best information forfurther processing. Embodiments are discussed herein in terms ofidentifying REEs but other elements may also be identified, such asgallium and germanium. Any reference to REE should be understood to alsorefer at least to gallium and germanium.

In embodiments, a method of predicting REE presence and abundance usesspectral analysis of a sample to detect key elements which arequantifiable by XRF and/or PGNAA. Measurements of these elements arethen used to predict REEs by a model that correlates presence of the keyelements with presence of REEs. Table 1 shows REEs that are predictedusing embodiments discussed herein in the left column. Elements that aremeasured using XRF are shown in the center column and elements that aremeasured using PGNAA are shown in the right column.

TABLE 1 Predicted REE XRF PGNAA La Na Al₂O₃ Ce Mg CaO Pr Al Fe₂O₃ Nd SiK₂O Pm P MgO Sm K MnO₂ Eu Ca Na₂O Gd Ti SiO₂ Tb Fe SO₃ Dy Ba TiO₂ Ho MnEr Sr Tm Yb Lu Ge Ga

XRF and PGNAA methods produce a spectrum from a sample. From thespectrum, the peaks are separated from the background (valleys/parts ofthe curves outside of the peaks). The peak areas are used to provideelemental compositions of a sample based on standard compositions. WhenXRF and PGNAA analyzers provide an output for elemental measurements,there are calibrations and algorithms used to process this peak areadata to output a final number for the amount of the element associatedwith each peak. The final elemental output measurement may be used tomake statistical correlations between elements and REE.

In embodiments, the full raw spectra captured by XRF or PGNAA is usedinstead of the calculated elemental measurements. A model is built, ortrained, using samples with known REE presence and abundance to generatecoefficients incorporating the background as well as the peaks of aspectrum. The background can also be understood as the valleys betweenpeaks. While coefficients are labeled with an element such as “Na, K,Ca, etc.” herein, the coefficients are not the final elemental outputmeasurements but the raw spectral values (including some of thebackground). As further samples are processed, neural networks or othercomputational analysis techniques may be used to continue to develop themodel algorithms using the original, or training, coefficients.

A general form of the model is represented as follows. Measurements madeby spectral analysis, either XRF or PGNAA, may include a measurementseries having a plurality of y individual XRF measurements, m₁, m₂, . .. m_(y). The model also includes a set of x predictions p₁, p₂, . . .p_(x), for REE abundance predictions of x REE species. Each predictionuses y fitting constants C, one for each individual measurement m, suchthat

$\begin{matrix}{p_{1} = {{m_{1}*C1_{1}} + {m_{2}*C1_{2}} + \ldots + {m_{y}*C1_{y}}}} & (1)\end{matrix}$ $\begin{matrix}{p_{2} = {{m_{1}*C2_{1}} + {m_{2}*C2_{2}} + \ldots + {m_{y}*C2_{y}}}} & (2)\end{matrix}$ … $\begin{matrix}{p_{x} = {{m_{1}*Cx_{1}} + {m_{2}*Cx_{2}} + \ldots + {m_{y}*C{x_{y}.}}}} & (3)\end{matrix}$

The value of each fitting constant is variable and is fit based on ameasured abundance for each REE using a complementary technique, forexample ICP-OES. The set of all y*x=N fitting constants can be fit usinga training set where the measured abundance of each of the x REE speciesis known experimentally. The model, using these fitting constants, isthen used to predict the presence of one or more of the x REE speciesusing a measurement series recorded for a new sample. In embodiments, aneural network may be used to generate the model.

FIG. 1 is a table of constants for use with the model represented byequations (1)-(3) for measurements generated using PGNAA. FIG. 2A is atable of constants for use with the model represented by equations(1)-(3) for measurements generated using XRF. FIG. 2B is a table ofmeasurements from FIG. 2A for HREE, LREE, total REE and LREE/HREE. Thespecific constants shown in FIGS. 1, 2A and 2B are to illustrateprinciples disclosed herein and other constants and elements may beused. The rows illustrate represent a set of constants C1₁, C1₂, . . .C1_(y) for an REE species x. The columns represent a set of constantsC1₁, C2₁, . . . Cx₁ for a measured element. Each cell indicates thevalue of a given constant after fitting to a set of samples with REEvalues measured with ICP-OES. Cells in boldface type are in the top 25%of measurements to the REE prediction and cells in italic are in thebottom 25%. Any measured sample may include a subset of the elementsshown in FIGS. 1, 2A and 2B, as well as additional elements. Further,any REE of interest may be predicted from a subset of the elements shownin FIGS. 1 and 2 , or from additional elements.

In an embodiment, the model described above includes using the fittingcoefficients as exponents in addition to multipliers. For example, giventhe same individual measurements m₁, m₂, . . . m_(y), a set ofprediction p₁, p₂, . . . p_(x) is calculated using two sets of fittingcoefficients C and D, both having y*x=N coefficients in the set, suchthat

$\begin{matrix}{p_{1} = {{C1_{1}*\left( m_{1} \right)^{D1_{1}}} + {C1_{2}*\left( m_{2} \right)^{D1_{2}}} + \ldots + {C1_{y}*\left( m_{y} \right)^{D1_{y}}}}} & (4)\end{matrix}$ $\begin{matrix}{p_{2} = {{C2_{1}*\left( m_{1} \right)^{D2_{1}}} + {C2_{2}*\left( m_{2} \right)^{D2_{2}}} + \ldots + {C2_{y}*\left( m_{y} \right)^{D2_{y}}}}} & (5)\end{matrix}$ … $\begin{matrix}{p_{x} = {{Cx_{1}*\left( m_{1} \right)^{Dx_{1}}} + {Cx_{2}*\left( m_{2} \right)^{Dx_{2}}} + \ldots + {{Cx}_{y}*{\left( m_{y} \right)^{Dx_{y}}.}}}} & (6)\end{matrix}$

In an embodiment, each measurement is normalized to a value between zeroand one based at least in part on a peak-value measured for thatmeasurement of the measurement series.

In an embodiment, the predicted abundances include one or more of totalrare earth element, light rare earth element, heavy rare earth element,the ratio of light rare earth element to heavy rare earth element,lanthanum, cerium, praseodymium, neodymium, samarium, europium,gadolinium, scandium, yttrium, terbium, dysprosium, holmium, erbium,thulium, ytterbium, and lutetium.

FIG. 3 is a flowchart illustrating a method 300 for predicting thepresence and abundance of rare earth elements. Method 300 may includeblocks 302, 308 and 312. In embodiments, the block 302 includes one ofblocks 304 or 306, block 308 includes block 310, and block 312 includesblock 314.

In block 302, feedstock samples are measured to generate measurements ofelements of interest in the sample. In an example of block 302, elementsof interest are measured using a spectrum analyzer and include elementsthat have a lower atomic weight than rare earth elements. Block 304includes measuring the feedstock sample using x-ray fluorescence. Block306 includes measuring the feedstock sample using prompt-gamma neutronactivation analysis. Embodiments discussed herein may use either or bothtypes of spectrum analysis.

In block 308, the measurements are correlated using a model. In block310, the model includes a corresponding fitting constant for eachelement of interest. In an example of block 310, the fitting constantsare similar to those shown in FIGS. 1 and 2 .

In block 312 the presence of one or more rare earth elements ispredicted based at least in part on the model correlation. In block 314,the rare earth elements may include one or more of total rare earthelement, light rare earth elements, heavy rare earth elements, and theratio of light rare earth elements to heavy rare earth elements. In anexample of blocks 312 and 314, the processor 130 predicts the presenceof rare earth elements based at least in part on the correlation ofspectrum analyzer measurements of a feedstock sample with coefficientsof the model.

The method 300 is not limited, unless otherwise specified or understoodby those of ordinary skill in the art, to the order shown in FIG. 3 .

FIG. 4 illustrates a system 400 for predicting the abundance of rareearth elements using measuring instrument 410, a processor 430, and amemory 440. The memory includes instructions 442, model 444, andpredictions 448. The measuring instrument 410 records a measurementseries of a feedstock sample 420 to generate corresponding measurementdata 450 that, in an embodiment, is stored in memory 440. Theinstructions 442, when executed by the processor 430 cause the processor430 to correlate the measurement series using the model 444 to generatepredictions 448. The processor 430 then predicts the presence of one ormore rare earth elements based at least in part on the predictions 448.In an embodiment, measuring instrument 410 is a handheld X-rayfluorescence instrument (XRF). In an embodiment, measuring instrument410 is a prompt gamma neutron activation analysis (PGNAA) sensor.

In an embodiment, the model 444 includes a plurality of fittingconstants 446 that include one fitting constant 446 for each measurementof a measurement series. Each fitting constant 446 is multiplied by thecorresponding measurement. In an embodiment, one or more of themeasurements is raised to the respective power of the correspondingfitting constant.

In an embodiment, the model 444 includes a plurality of fittingconstants 447 that include fitting constant 447 for ash content data.Each fitting constant 447 is multiplied by a corresponding measurementof ash content data. In an embodiment, one or more of the measurementsis raised to the respective power of the corresponding fitting constant.

In an embodiment, the measuring instrument 410 records a measurement ofthe sample 420 that includes an abundance measurement of elements ofinterest, which may include one or more of sodium, magnesium, aluminum,silicon, phosphorus, sulfur, potassium, calcium, titanium, iron, barium,strontium, manganese and yttrium. In embodiments, other elements may bemeasured. Measuring instrument 410 may record a single measurement fromsample 420, or a series of measurements. In an embodiment, themeasurement data 450 of sample 420 recorded by measurement instrument410 is combined with ash content data 452. The instructions 442, whenexecuted by the processor 430 cause the processor 430 to correlate themeasurement data 450 and ash content data 452 using the model 444 thatincludes fitting constants 446 and fitting constants 447 to generatepredictions 448. The processor 430 then predicts the presence of one ormore rare earth elements based at least in part on the predictions 448.

In an embodiment, the measuring instrument 410 records a measurement ofthe sample 420 that includes an abundance measurement of silicon,titanium and barium. By including fewer species in the measurementseries, system 400 is more efficient in one or more of energyconsumption, time, and processing power.

In an embodiment, measuring instrument 410 is a prompt gamma neutronactivation analysis (PGNAA) sensor that is communicatively coupled tothe processor 430 and that records a measurement of sample 420 togenerate corresponding measurement series data 450, that in anembodiment is stored in memory 440. The sample 420 may be present on aconveyor belt and the system 400 measuring instrument 410 can beoperated in combination with selective mining to perform real-timeon-belt coal sorting.

Changes may be made in the above methods and systems without departingfrom the scope hereof. It should thus be noted that the matter containedin the above description or shown in the accompanying drawings should beinterpreted as illustrative and not in a limiting sense. The followingclaims are intended to cover all generic and specific features describedherein, as well as all statements of the scope of the present method andsystem, which, as a matter of language, might be said to falltherebetween.

1-20. (canceled)
 21. A system for predicting the presence of rare earthelements (REEs) in a feedstock, comprising: a spectrum analyzer operablyconfigured to measure a sample of the feedstock and record a measurementdata for the feedstock sample; a processor communicatively coupled tothe spectrum analyzer, the processor configured to process themeasurement data; and a memory assembly communicatively coupled to thespectrum analyzer and the processor, the memory assembly comprising oneor more machine readable instructions, one or more models, and one ormore predictions; wherein the one or more instructions, when executed bythe processor, cause the processor to measure the feedstock sample usingthe spectrum analyzer to generate the measurement data corresponding toelements of interest with a lower atomic weight than REEs, correlate themeasurement data using the one or more models, and predict the presenceof one or more REEs based at least in part on the correlation.
 22. Thesystem of claim 21, wherein the spectrum analyzer is an X-rayfluorescence instrument.
 23. The system of claim 22, wherein theelements of interest include one or more of sodium, magnesium, aluminum,silicon, phosphorous, potassium, calcium, titanium, iron, barium,manganese and strontium.
 24. The system of claim 23, wherein the modelcomprises a corresponding fitting constant for each element of interest.25. The system of claim 21, wherein the spectrum analyzer is a hand-heldX-ray fluorescence instrument.
 26. The system of claim 21, wherein thespectrum analyzer is a prompt gamma neutron activation analysis (PGNAA)sensor.
 27. The system of claim 26, wherein the elements of interestinclude one or more of Al₂O₃, CaO, Fe₂O₃, K₂O, MgO, MnO₂, Na₂O, SiO₂,SO₃ and TiO₂.
 28. The system of claim 27, wherein the model comprises acorresponding fitting constant for each element of interest.
 29. Thesystem of claim 21, wherein the model comprises a corresponding fittingconstant for each element of interest.
 30. The system of claim 29,wherein the model comprises an ash fitting constant corresponding to anash content.
 31. The system of claim 29, wherein the elements ofinterest include one or more of sodium, magnesium, aluminum, silicon,phosphorous, sulfur, potassium, calcium, iron, strontium, manganese andyttrium.
 32. The system of claim 29, wherein the elements of interestinclude one or more of silicon, titanium, barium and gallium.
 33. Thesystem of claim 21, wherein the REEs comprise one or more of total rareearth elements, light rare earth elements, heavy rare earth elements,and a ratio of light rare earth elements to heavy rare earth elements.34. The system of claim 21, wherein the feedstock further comprises coaland coal byproducts.
 35. The system of claim 34, wherein the coalbyproducts further comprise ash content.
 36. The system of claim 21,wherein the spectrum analyzer is operably located a conveyor beltconfigured to transport the feedstock from a first location to a secondlocation.
 37. The system of claim 36, wherein the feedstock is coal andcoal byproducts and the spectrum analyzer is configured to performreal-time on-belt coal sorting based upon the prediction of one or moreREEs in the feedstock sample.
 38. The system of claim 21, wherein thespectrum analyzer captures a full raw spectra from the feedstock samplethat comprises the elements of interest.
 39. The system of claim 21,wherein the REEs comprise one or more of lanthanum, cerium,praseodymium, neodymium, promethium, samarium, europium, gadolinium,terbium, dysprosium, holmium, erbium, thulium, ytterbium, lutetium,yttrium, scandium, germanium and gallium.
 40. The system of claim 21,wherein the measurement data is configured to be stored in the memory.